Plant root systems,a crucial component of biogeotechnics,have been recognized as a promising and sustainable strategy to address novel challenges in geotechnical engineering,i.e.,climate change(Ng et al.,2022).Root-so...Plant root systems,a crucial component of biogeotechnics,have been recognized as a promising and sustainable strategy to address novel challenges in geotechnical engineering,i.e.,climate change(Ng et al.,2022).Root-soil composite and root-reinforced slopes have re-ceived widespread attention in recent decades,due to the ability of root to regulate soil properties through mechanical reinforcement and hy-draulic transpiration(Li&Duan,2023;Ni et al.,2024).Fig.1 provides a co-occurrence network plot of plant root-based soil reinforcement strategies published over the last decade,where three clusters are identified with different colors.On the left of the network map,clusters in red and blue are primarily driven by geotechnical investigations of vegetated slopes(i.e.,plant root reinforced slopes)and root-soil com-posite/root-permeated soils,as denoted by the terms like"model","test","slope","strength"and"vegetation",while the green cluster on the right side demonstrates botany-related domains,for instance,"plant growth",Indeed,the reinforcement of vegetated soil strength is com-plex and varies significantly with an abundance of factors,both me-chanically and hydraulically.Particularly,the impact of root mor-phology and architecture cannot be negligible,including keywords"root area ratio"root distribution""root morphology"root diame-ter"root density"in Fig.1 with the root size and root depth ranking foremost.展开更多
Artificial intelligence(AI)in education is experiencing a transformative shift,fueled by foundation models with unprecedented capabilities.These advancements are reshaping educational paradigms and addressing challeng...Artificial intelligence(AI)in education is experiencing a transformative shift,fueled by foundation models with unprecedented capabilities.These advancements are reshaping educational paradigms and addressing challenges such as diverse student needs,resource gaps,and engagement.1 This paper examines three key trends:the shift from perception to cognition,the transition from generalized to personalized learning,and the rise of multimodal systems,as shown in Figure 1.Together,these trends open up new opportunities to tackle persistent challenges in the education sector.展开更多
To the Editor,We read with great interest the recent Editorial by Li et al[1]on the potential of large language models(LLMs)in clinical decision support.Their assessment of the LLMs’strengths in data processing,diagn...To the Editor,We read with great interest the recent Editorial by Li et al[1]on the potential of large language models(LLMs)in clinical decision support.Their assessment of the LLMs’strengths in data processing,diagnostics,and workflow optimization is timely and well-grounded.We write to extend the discussion to a key limitation the authors themselves acknowledge:the lack of emotional intelligence in current AI systems.As Li et al[1]noted,LLMs remain“notably deficient in addressing emotional and ethical aspects.”展开更多
基金supported by Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-LZX0001)High-end Foreign Expert Introduction program(No.G2022165004L)+1 种基金High-end Foreign Expert Introduction program(No.DL2021165001L)The fi-nancial supports are gratefully acknowledged.
文摘Plant root systems,a crucial component of biogeotechnics,have been recognized as a promising and sustainable strategy to address novel challenges in geotechnical engineering,i.e.,climate change(Ng et al.,2022).Root-soil composite and root-reinforced slopes have re-ceived widespread attention in recent decades,due to the ability of root to regulate soil properties through mechanical reinforcement and hy-draulic transpiration(Li&Duan,2023;Ni et al.,2024).Fig.1 provides a co-occurrence network plot of plant root-based soil reinforcement strategies published over the last decade,where three clusters are identified with different colors.On the left of the network map,clusters in red and blue are primarily driven by geotechnical investigations of vegetated slopes(i.e.,plant root reinforced slopes)and root-soil com-posite/root-permeated soils,as denoted by the terms like"model","test","slope","strength"and"vegetation",while the green cluster on the right side demonstrates botany-related domains,for instance,"plant growth",Indeed,the reinforcement of vegetated soil strength is com-plex and varies significantly with an abundance of factors,both me-chanically and hydraulically.Particularly,the impact of root mor-phology and architecture cannot be negligible,including keywords"root area ratio"root distribution""root morphology"root diame-ter"root density"in Fig.1 with the root size and root depth ranking foremost.
基金funded by the NSFC(no.62172393)the Major Public Welfare Project of Henan Province(no.201300311200).
文摘Artificial intelligence(AI)in education is experiencing a transformative shift,fueled by foundation models with unprecedented capabilities.These advancements are reshaping educational paradigms and addressing challenges such as diverse student needs,resource gaps,and engagement.1 This paper examines three key trends:the shift from perception to cognition,the transition from generalized to personalized learning,and the rise of multimodal systems,as shown in Figure 1.Together,these trends open up new opportunities to tackle persistent challenges in the education sector.
文摘To the Editor,We read with great interest the recent Editorial by Li et al[1]on the potential of large language models(LLMs)in clinical decision support.Their assessment of the LLMs’strengths in data processing,diagnostics,and workflow optimization is timely and well-grounded.We write to extend the discussion to a key limitation the authors themselves acknowledge:the lack of emotional intelligence in current AI systems.As Li et al[1]noted,LLMs remain“notably deficient in addressing emotional and ethical aspects.”
